496results about "Adaptive network" patented technology

The invention provides a self-adaptive signal filtering method and system based on multi-band fusion, and the method comprises the steps: firstly obtaining a multi-band signal set containing original signal components of different bands in a target environment, carrying out the frequency band decomposition of the multi-band signal set to obtain a plurality of sub-band signals, and then carrying out the self-adaptive filtering operation of each sub-band signal, the method comprises the following steps: dynamically adjusting a filtering coefficient according to noise characteristics of a sub-band signal, generating a filtered sub-band signal, carrying out multi-band fusion on the filtered sub-band signal to obtain a target filtering signal, and finally adjusting a segmentation strategy of band decomposition or a dynamic adjustment rule of adaptive filtering according to a quality evaluation result of the target filtering signal. And outputting the optimized target filtering signal, thereby effectively coping with a complex multi-band signal, adaptively adjusting the filtering process, and improving the quality and stability of the filtering signal.

The invention relates to the technical field of data processing, in particular to an anti-interference six-dimensional sensor acquisition system, which comprises a multi-modal signal fusion module for receiving original acquisition data of a six-dimensional sensor and realizing fusion of the acquisition data of all sensors based on a self-adaptive dynamic weight distribution algorithm; the intelligent error compensation module is used for performing forward compensation on errors in the six-dimensional sensor data so as to improve the accuracy of the data; the correction feedback module is used for carrying out self-correction on the data acquired by the six-dimensional sensor at an application end and carrying out feedback adjustment on sampling parameters according to a correction result; according to the method, a self-adaptive covariance matched filtering algorithm is adopted, a noise covariance matrix is dynamically adjusted and measured in combination with Kalman filtering, the denoising effect is improved, and the optimal signal quality is ensured to be kept under different noise levels by setting a dynamic smoothing factor and adaptively adjusting a data smoothing process.

The invention provides a satellite navigation interference detection and suppression system and method, and belongs to the technical field of signal analysis. The sensor module is used for determining a target area where a sensor is installed according to a target detection sky, and deploying and installing the sensor in the target area to obtain original signal data of a satellite; the signal processing module is used for standardizing the original signal data to obtain standard signal data and converting the standard signal data into a first frequency spectrum; the feature recognition module is used for analyzing features of the first frequency spectrum through fast Fourier transform and screening signal points with abnormal fluctuation; the interference analysis module is used for classifying the features of all the signal points and matching corresponding interference categories to obtain all interference categories of the original signal data; and the interference suppression module is used for adjusting parameters in real time to reduce interference by using an adaptive filter according to the dynamic characteristics of the interference category. The reliability of navigation signals is improved, and the anti-interference capability is enhanced.

The invention relates to the technical field of power monitoring, in particular to a method and system for detecting and positioning partial discharge of a switch cabinet, and the system comprises a signal collection module, a signal preprocessing module, a feature extraction module, a data fusion module, a discharge source positioning module and a classification and alarm module. The signal acquisition module simultaneously acquires partial discharge signals through a plurality of sensors installed in the switch cabinet, the signal preprocessing module performs noise filtering and signal enhancement on the acquired signals, and the feature extraction module screens out most representative discharge features based on a deep learning algorithm. The data fusion module fuses data of multiple sensors through Kalman filtering and a Bayesian reasoning algorithm, the discharge source positioning module accurately calculates the position of a discharge source through a multi-point positioning algorithm, and the discharge source is classified through the classification and alarm module. The partial discharge detection precision and the positioning accuracy of the switch cabinet can be effectively improved, and safe operation of a power system is ensured.

An adaptive variable step-size LMS filter based on a hyperbolic tangent function and its filtering method and computer equipment belong to the field of filter technology, and solve the problem that the LMS algorithm improved based on a nonlinear function requires too many manual adjustments of parameters and that the convergence speed and convergence accuracy of the traditional LMS algorithm cannot be satisfied simultaneously. The method of the present invention includes: determining the filter tap length and the number of iterations; initializing the filter parameters; obtaining the filter input signal vector; determining the instantaneous expected signal of the filter; obtaining the instantaneous output signal; obtaining the instantaneous error signal of the filter and the accumulated amount of the error signal based on the instantaneous output signal; obtaining the instantaneous step size using a step-size update formula based on a hyperbolic tangent function; and continuously iterating the filter tap weight coefficient vector to construct an LMS filter using the filter tap weight coefficient vector obtained after the number of iterations. The present invention is suitable for improving the performance of the LMS filter, and the filter of the present invention can be widely used in various signal processing fields.

The invention relates to the technical field of narrow-band filters, and discloses an ultra-low power consumption narrow-band filter implementation method based on a digital phased array system, and the method comprises the following steps: S1, collecting input signal data, carrying out the adaptive preprocessing, and generating preprocessed signal data; s2, performing digital beam forming processing based on the preprocessed signal data to generate digital beam forming data; s3, performing narrowband filtering parameter matching processing according to the digital beam forming data to generate narrowband filtering parameter data; in the narrowband filtering processing process, disturbance of environmental electromagnetic interference to a target signal is identified and eliminated in real time through adaptive noise suppression and a multi-dimensional signal preprocessing mechanism, so that the system can guarantee the pointing precision of digital beam forming, the pointing offset problem in the space signal capturing process is avoided, the accuracy of narrowband signal extraction is improved, and the accuracy of narrowband signal extraction is improved. And the reliability of filtering output and the signal fidelity are enhanced.

The invention relates to the technical field of adaptive filtering, and discloses a deviation compensation constraint adaptive filtering method and system based on error nonlinearity, and the method comprises the steps: constructing an adaptive weight vector and a noise-containing input signal vector according to an adaptive weight and a noise-containing voice signal sampling value when the weight of an adaptive filter is subjected to linear constraint; after the inner product generates an output signal, an estimation error signal is generated, and an error square median is calculated; calculating an error signal variance estimated value and a self-adaptive weight vector power estimated value according to the error square median and the self-adaptive weight vector, and then calculating an input noise variance estimated value; calculating a deviation compensation item according to the adaptive weight vector and the input noise variance estimation value, and updating the adaptive weight vector with the noise-containing input signal vector and the estimation error signal; and calculating the inner product of the updated adaptive weight vector influenced by the low-efficiency input noise and the noise-containing input signal vector to obtain an output signal subjected to input noise deviation compensation.

The invention provides a filter passband fluctuation automatic compensation method and system. The method comprises the following steps: acquiring multi-dimensional tensor data of filter data; extracting features of the multi-dimensional tensor data to obtain a time-frequency domain joint feature matrix; the time-frequency domain joint feature matrix is input into a preset double-branch LSTM network, the preset double-branch LSTM network comprises a main branch and an auxiliary branch, a first adjustment parameter set and a hidden state vector are generated through the main branch, reverse correction is performed on the first adjustment parameter set according to the hidden state vector through the auxiliary branch, and compensation parameters are generated; performing dynamic coupling mapping according to the compensation parameters and preset physical constraint conditions to obtain a constrained parameter set; and controlling corresponding hardware to carry out passband fluctuation compensation on the filter based on the constrained parameter set.

The invention relates to the technical field of adaptive filters, and discloses a wide linear adaptive filter, which comprises the following steps of: performing tensor integral solution on a system weight vector at the moment of the wide linear adaptive filter to obtain two adaptive weight vectors; constructing an input matrix based on the sampling value of the input signal, and calculating an estimation signal of the moment based on the product of the input matrix of the moment and the two adaptive weight vectors; and calculating the difference value between the expected signal and the estimation signal at the moment, obtaining an estimation error signal, and updating the gain vector at the obtaining moment and the estimation error signal by using a minimum error mode criterion, updating the two adaptive weight vectors at the obtaining moment, and reconstructing the system weight vector at the moment of the wide linear adaptive filter. According to the invention, the recursive adaptive filtering algorithm resistant to impulse noise interference is realized with low calculation complexity by using weight decomposition and selection of a robust cost function.

The invention provides a highway bridge joint vibration and deformation condition monitoring method, and particularly relates to the technical field of bridge structure health monitoring, and the method comprises the steps: laying a fiber bragg grating sensor array at a bridge joint, and the array comprises a plurality of fiber bragg grating sensors which are distributed at intervals along the length direction of the joint; and each sensor is connected with the joint filling body contact surface through a pre-embedded fixing structure. The sensor array collects strain signals and vibration signals in the joint in real time, analog-to-digital conversion and caching are completed through the data collection unit, digital sensing signals are generated and stored, and a historical monitoring data set is constructed. A noise suppression algorithm is adopted to filter the signal, and ambient temperature and humidity and electromagnetic noise interference are eliminated. Features are extracted based on a data fusion algorithm, and crack feature parameters reflecting the seam fine crack expansion trend are generated. A historical monitoring data set and the parameters are combined, a supervised learning model is used for calculating a dynamic adjustment threshold value, a real-time monitoring early warning signal is generated through comparison, and early-stage high-sensitivity monitoring of the seam is achieved.

The invention discloses an SER6 Pro antenna with adaptive beam forming capability, comprising: a generation module for generating an initial beam directional diagram based on spatial-temporal correlation analysis of multipath signals; the calculation module is used for updating beam direction parameters in real time by using a Doppler frequency shift estimation algorithm so as to adapt to Doppler frequency shift change of a high-speed mobile user; the suppression module is used for carrying out noise suppression on the beam signal in a frequency domain by adopting an adaptive filter; and the optimization module is used for dynamically adjusting the beam forming matrix and optimizing the beam direction and intensity through an iterative optimization algorithm. The method has the beneficial effects that the problem of inaccurate beam direction identification caused by excessive reflected and scattered signals in a complex multi-path propagation environment is solved; the problem of unstable beam tracking caused by significant change of Doppler frequency shift in a high-speed mobile user scene is solved; and the problem that the signal-to-noise ratio is reduced due to the strong external noise source in a high-interference environment is solved.

The invention discloses a semi-supervised learning method based on a self-adaptive threshold value, belongs to the field of radio communication, and aims to extract general semantic features of signals through comparative learning so as to improve generalization of downstream modulation identification tasks. A self-adaptive threshold mechanism is introduced to generate a high-quality pseudo tag, and confirmation deviation is reduced through additional category information, so that the robustness of the model is improved; besides, a hierarchical encoder capable of learning Fourier filtering is designed to capture multi-scale semantic features of large-scale long-sequence signals, improve the calculation efficiency of a model and research and verify an algorithm based on a plurality of large-scale data sets, so that the accuracy of automatic modulation recognition is remarkably improved, and particularly, under the condition of less label data, the accuracy of automatic modulation recognition is greatly improved. Compared with a mainstream method, the method has higher generalization ability and robustness, and can effectively cope with signal diversity and noise interference in a real communication environment.

The invention relates to the technical field of adaptive filtering, discloses a variable-step robust adaptive filter and a filter network, and aims to solve the technical problem that a traditional adaptive filter cannot effectively consider convergence speed, steady-state precision and impulse noise interference resistance. A non-linear error factor is introduced, large errors caused by impulse noise are effectively suppressed through a generalized function form of the non-linear error factor, and the robustness of the filtering process is remarkably improved; meanwhile, a variable step length mechanism is provided, the theoretically optimal candidate step length is calculated on line by minimizing the mean square deviation of the next moment, and a self-adaptive target variable step length is generated through truncation and time smoothing processing, so that dynamic balance is realized between rapid convergence and low-steady-state maladjustment. According to the invention, the filter is expanded to the distributed network, each node follows a diffusion strategy of first updating and then combining, and global information sharing and performance collaborative optimization are realized by using a combined coefficient.

The invention discloses a steel rolling process-based bar temperature filtering control method, which relates to the technical field of steel rolling production and comprises the following steps of: acquiring bar temperature original data, corresponding steel rolling process parameters and steel grade component data in advance; and a material temperature feature library is constructed, steel grade components are identified in real time according to an online spectrum analysis technology, and a matched filtering parameter reference template is called. By constructing the material temperature feature library and combining the online spectrum analysis technology, accurate recognition of different steel types and intelligent calling of the matched filtering parameter template are achieved, the problem that a traditional filtering method cannot adapt to the characteristic difference of the steel types is effectively solved, the universality and pertinence of filtering control are remarkably improved, and the method is suitable for large-scale popularization and application. Effective smoothness of high-fluctuation temperature data is guaranteed, the problem of lag or excessive filtering caused by a fixed window is avoided, and the dynamic adaptability of the system to a complex steel rolling scene is greatly enhanced.

The invention provides a construction method and device of an adaptive filtering model and a filtering method. According to the construction method of the adaptive filtering model provided by the invention, the adaptive filtering model can be constructed, and the adaptive filtering model can filter the real-time monitoring signal monitored by the source equipment in real time in an actual service state under the condition that statistical characteristics of the signal and noise do not need to be counted; besides, modeling noise signals and random noise are introduced into the self-adaptive filtering model, so that the trained self-adaptive filtering model can effectively filter interference signals under the working frequency of noise source equipment, and meanwhile, the denoising capability of the self-adaptive filtering model on the random noise can be enhanced by introducing the random noise; furthermore, through adversarial training, continuous learning and experience accumulation can be carried out by using the strong learning ability and intelligent characteristics of the neural network, so that the trained adaptive filtering model better adapts to dynamic filtering requirements in different environments.

The invention discloses a self-adaptive filtering method and system for an intelligent electric energy meter, and relates to the technical field of power grid signal processing, and the method comprises the steps: obtaining a power grid voltage and current signal set collected by a target intelligent electric energy meter historically, and obtaining a signal noise spectrum data set and a noise level evaluation result through spectrum analysis and noise level evaluation; if the result exceeds a preset noise threshold value, identifying an interference type and building a noise adaptive filtering multi-channel, integrating and deploying the noise adaptive filtering multi-channel into a target intelligent electric meter to complete signal filtering test and effect evaluation, and dynamically optimizing and generating an electric energy meter adaptive filtering multi-channel based on the obtained multi-channel filtering effect index parameter; and finally, signal filtering closed-loop control is realized. The technical problem that a traditional electric energy meter is weak in filtering pertinence and cannot adapt to dynamic noise of a power grid in electric power related scenes with complex dynamic interference is solved, and the technical effects that the electric variable measurement accuracy of the electric energy meter is improved, and the adaptive capacity of the electric energy meter to different interference scenes is enhanced are achieved.

The invention relates to an industrial internet photoelectric sensor precision control method and system and a readable medium, and aims to solve the problems of signal instability and measurement precision reduction caused by interference of temperature, humidity, vibration and the like of a photoelectric sensor in a complex industrial environment. The method comprises the steps of signal acquisition, signal preprocessing, error compensation, dynamic optimization, feedback control and the like, wherein denoising processing is performed on signals through an adaptive filtering algorithm, and random noise is eliminated; calculating an error compensation factor based on the multi-dimensional environmental parameters, and correcting the external interference influence of the signal through a sensitivity model; and the gain and exposure time of the photoelectric sensor are dynamically adjusted by using a closed-loop control algorithm, so that signal output is optimized. Besides, the system adopts an industrial internet communication protocol and is deeply fused with a cloud platform, so that remote monitoring, parameter optimization and self-learning capabilities are realized. The photoelectric sensor is suitable for complex scenes such as optical element detection, industrial robot control and wind power generation monitoring, and the anti-interference capability and the measurement precision of the photoelectric sensor are remarkably improved.

The invention belongs to the field of time synchronization and data processing, and discloses a satellite and Rowland timing data fusion method and related device.Firstly, original data are intercepted and subjected to mean value removal to eliminate baseline offset, and a frequency domain matrix is reconstructed by combining frequency domain conversion with singular value decomposition; according to the method, periodic term interference signals in a frequency domain are accurately recognized and filtered out through a dynamic threshold strategy, then effective components are reserved through time domain conversion, finally, a noise covariance matrix and observation model parameters are dynamically adjusted based on an adaptive Kalman filtering algorithm, and dynamic weight fusion of double-source data is achieved. By the adoption of the method, the defects that a traditional weighted average method is insensitive in fixed weight, Kalman filtering parameters are rigid and global interference suppression of wavelet transformation is insufficient are effectively overcome, the suppression capacity for non-stationary noise and periodic interference is remarkably improved, fused data have the high precision of a satellite system and the anti-interference characteristic of a Rowland system, and the method is suitable for being applied to the field of satellite communication. And finally, high-reliability and high-stability time synchronization performance is realized.

The invention relates to the technical field of data signal processing, in particular to a conductivity meter data processing method based on sage-husa self-adaptive Kalman filtering, which comprises the following steps of: sampling conductivity and temperature original data of a measured object through a conductivity sensor probe; an improved sage-husa adaptive Kalman filtering model of the conductivity meter is established; the method comprises the following steps of: transmitting original data measured by a probe of a conductivity sensor into a sage-husa self-adaptive filter, and performing filtering and noise reduction processing on an original signal by using an improved sage-husa self-adaptive Kalman filtering algorithm; the sage-husa adaptive filter can dynamically adjust filter parameters according to the change of the system state and the difference of noise characteristics, a system dynamic model can be well reflected without setting an accurate initial value so as to obtain more accurate estimation data, the robustness of the system is enhanced through outlier judgment introduced during innovation calculation, and the robustness of the system is improved. The measurement precision is effectively improved, the risk that parameters are affected by outliers is reduced, and the real-time performance of the system is enhanced.

The invention discloses an acceleration signal processing method, device and equipment for measurement while drilling and a medium. The method comprises the following steps: processing an acceleration signal by using two alternating filters to obtain two alternating filtering results; carrying out weighted summation on the two alternative filtering results by adopting a first weight and a second weight to obtain a weighted summation result; and performing adaptive adjustment on the first weight and the second weight based on the weighted summation result and an optimization target so as to obtain an optimized weighted summation result based on the optimized first weight and the optimized second weight after adaptive adjustment, and taking the optimized weighted summation result as a processing result of the acceleration signal. Accuracy and stability of acceleration signal processing results can be improved, noise components can be reduced, measurement precision of well track parameters such as well deviation can be improved, the drilling direction can be accurately fed back and adjusted, and the drilling rate of high-quality reservoirs can be effectively guaranteed.

The invention provides a reduced Gaussian kernel adaptive filtering method and system based on nearest center estimation, and relates to the field of data processing, and the method comprises the steps: obtaining an initial parameter and a training set, and constructing an adaptive filtering system based on the initial parameter; performing cluster division on an input signal, obtaining a feature vector of the input signal by calculating a reduced Gaussian kernel, and selecting a corresponding sub-filter according to a cluster for prediction to obtain prediction output; constructing a target optimization problem according to a maximum correlation entropy criterion based on an error between prediction output and expected output, and converting the target optimization problem into a convex optimization problem; solving a convex optimization problem by hybridizing three conjugate gradients, and updating the weight of a sub-filter; and repeating the steps to converge the error of the adaptive filtering system, and completing the adaptive filtering process. According to the method, the problem of dimension disasters existing in a traditional feature mapping method is solved by reducing the Gaussian kernel, the calculation complexity is remarkably reduced, and meanwhile unnecessary calculation overhead is greatly reduced through nearest center estimation.

The invention discloses a sampling signal noise reduction method and device based on a wavelet basis function, equipment and a medium, and belongs to the field of signal processing, and the method comprises the steps: carrying out the preprocessing of an original sampling signal after the original sampling signal is obtained, and obtaining a processing signal; performing wavelet decomposition on the processing signal to obtain a wavelet coefficient, and correcting the wavelet coefficient to obtain a correction coefficient; and performing inverse wavelet transform on the correction coefficient to obtain a noise reduction signal. According to the invention, after the original sampling signal is obtained, the preset composite power quality disturbance signal is added to the original sampling signal, and the noise reduction processing is carried out according to the wavelet coefficient of the signal combined with the noise to obtain the noise reduction signal. According to the invention, by adding the noise, high-efficiency noise suppression can be realized while key disturbance characteristics are reserved, so that the precision of the signal after noise reduction is improved; as the signal features of the original signals are reserved, a reliable preprocessing scheme can be provided for high-precision data acquisition scenes such as new energy monitoring of the power grid according to the original features subsequently.

The invention relates to the technical field of digital signal generation, in particular to a broadband sine wave digital signal generation method and system, and the method comprises the steps: obtaining an initial broadband sine wave digital signal; adopting a signal decomposition algorithm to obtain each modal component and residual component of the initial broadband sine wave digital signal; determining an interference influence factor of each modal component; determining a period difference degree and a superposition influence coefficient of each modal component; determining a relative difference factor and a relative influence coefficient of each modal component; determining a step parameter of each modal component; and in combination with an adaptive filtering algorithm, reconstructing the filtered modal component and residual component to obtain a de-noised broadband sine wave digital signal. According to the invention, the step length parameter of the adaptive filtering algorithm can be adjusted, the denoising effect of the adaptive filtering algorithm is improved, and then the high-quality broadband sine wave digital signal is generated.

The invention relates to the technical field of anti-dry-burning liquid level monitoring, in particular to an anti-dry-burning liquid level monitoring control system based on a PCB capacitance sensing circuit, in the anti-dry-burning liquid level monitoring control system based on the PCB capacitance sensing circuit, a signal preprocessing module adopts self-adaptive filtering to dynamically suppress noise interference in an original capacitance signal, and a reliable preprocessing signal is generated; the boiling state recognition module decomposes the signal into a steady state component and a transient state component, judges the boiling state according to the matching degree of the transient state component and a boiling feature library, and outputs a freezing instruction to maintain operation when the matching degree is high, so that mistaken shutdown is avoided; meanwhile, in combination with the judgment that the steady-state component is continuously lower than the warning line, a trigger instruction is output when the matching degree is low, and the real dry burning risk is accurately captured; the intelligent execution module responds instantly according to the instruction, guarantees continuous operation of equipment during freezing, executes power cut-off and sound-light alarm during triggering, and eliminates the potential safety hazard of dry burning; according to the process, accurate distinguishing between boiling interference and real dry burning is achieved, and equipment safety and operation continuity are synchronously optimized.

The invention relates to the technical field of telephone communication, and discloses a robust recursive least square adaptive filter based on inverse QR decomposition and a storage medium, and the method comprises the steps: calculating an inner product of an adaptive weight vector at a moment and an input signal vector, and obtaining an output signal at the moment of the adaptive filter; calculating a difference value between the expected signal and the output signal at the moment, obtaining an estimation error signal, and constructing a nonlinear weighting function; recursively updating and acquiring an inverse correlation matrix of the moment input signal vector based on the moment input signal vector and transpose thereof, a nonlinear weighting function, the inverse correlation matrix of the input signal vector and the covariance of the estimated error signal, and constructing a block matrix containing the inverse correlation matrix of the moment input signal vector; inverse QR matrix decomposition and unitary rotation operation are sequentially carried out on the block matrix, an array before inverse QR decomposition and an array after inverse QR decomposition at the moment are obtained, a gain vector at the moment is constructed, and a self-adaptive weight vector at the moment is updated and obtained.

The invention provides a fuze denoising method and system based on adaptive filtering, and relates to the technical field of signal processing. The method comprises the following steps: constructing a filter coefficient iteration model through constant parameter variation according to a variable step size algorithm model; calculating an error value between the input signal data and the output signal data of the filter; under the condition that the error value is not smaller than a preset error limit, iteratively updating a filter coefficient of the filter through a filter coefficient iteration model according to the current error value and a historical error value; and carrying out denoising processing on the signal data by using the updated filter coefficient. According to the method, the convergence speed of the model can be effectively increased, and meanwhile, the steady-state error performance can be relatively good. Moreover, historical error values at different moments before the current secondary error value can be introduced, so that the method has the advantage of high practicability, and can be applied to various different application scenes.

The invention relates to the technical field of distributed filtering control, and provides a distributed filtering control method and system for self-triggering communication, and the method comprises the following steps: obtaining a state estimation value of a current filter node, a state estimation value of an adjacent filter node, and a measurement result of a sensor system; predicting a self-triggering time interval according to the obtained state estimation values and measurement results; after the current filter node waits for the end of the predicted self-triggering time interval, communication is triggered, data interaction is conducted, and transmission data is obtained. The method is used for a self-triggering strategy of interaction between a sensor network and a filter network, the self-triggering strategy predicts the time interval of next data transmission according to the current measurement value, the calculation amount can be reduced, and meanwhile the communication overhead can be optimized.

The invention relates to the technical field of electronic circuits, in particular to a filter circuit, a voltage filter control circuit, a chip and electronic equipment. The filter circuit comprises a counter, a first data selector, a digital operation circuit and a register, the counter is used for receiving the counting enable signal, outputting a counting mark value to the first data selector, determining a corresponding current logic operation mode according to the current counting mark value, and performing logic operation on the intermediate input parameter according to the current logic operation mode to obtain a first voltage error value after filtering processing; by adopting the filter circuit provided by the invention, required logical operation can be completed by virtue of the first data selector and the digital operation circuit, so that the first voltage error value is output, and the filtered voltage signal is adjusted according to the first voltage error value, so that the set frequency component in the output voltage signal is filtered out; in this way, complex filter coefficients do not need to be configured, a processor with higher computing power does not need to be used, and hardware cost is saved.

The invention discloses a battery SOC prediction method and system based on dynamic electrochemical impedance spectroscopy measurement in the technical field of lithium battery state prediction, and the method comprises the steps: superposing DIBS square wave signals as excitation current input on the basis of the main charging and discharging current of a battery, and collecting the voltage and current data of the battery in real time; according to the impedance spectrum, fitting a fractional order equivalent circuit model by adopting a nonlinear least square optimization method to obtain initial parameters of the fractional order equivalent circuit model; the initial parameters and voltage and current data collected in real time are input into a self-adaptive dual augmented extended Kalman filter, the main filter and the secondary filter operate cooperatively, state prediction and parameter prediction are executed alternately, and real-time synchronous prediction of SOC and fractional order equivalent circuit model parameters is achieved. The method is suitable for the SOC estimation requirement of the battery under the dynamic working condition, the working state of the battery does not need to be interrupted, and the method has good linearity, robustness and adaptivity.

The invention provides a pulse signal adaptive filtering method and system based on multi-band signal entropy. The method comprises the following steps: receiving a plurality of satellite communication signals of different frequency bands, and extracting a phase offset, an amplitude value and a frequency value; and generating a multi-dimensional feature group of each frequency band, and extracting the signal-to-noise ratio and the frequency variation of the corresponding frequency band from the multi-dimensional feature group. And based on the signal-to-noise ratios and the frequency variations of all the frequency bands, dynamically adjusting filtering parameters through a preset three-stage tuning structure, and filtering to obtain a filtered signal. Transmitting the filtered signal to a federated learning framework, and calculating a multi-band signal entropy value by combining the multi-dimensional feature group; adaptive filtering parameters are optimized based on entropy values, global aggregation is carried out after differential privacy processing, and an aggregation feature graph group is generated; and through a channel and a space attention mechanism, effective information of a high-entropy frequency band is enhanced, noise of a low-entropy frequency band is suppressed, and pulse signal adaptive filtering based on multi-frequency-band signal entropy is completed. According to the technical scheme provided by the invention, the accuracy of adaptive filtering of the pulse signal is improved.